Open AccessDetection and isolation of false data injection attack for smart grids via unknown input observers
Author(s) -
Luo Xiaoyuan,
Wang Xinyu,
Pan Xueyang,
Guan Xinping
Publication year - 2019
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2018.5139
Subject(s) - computer science , kalman filter , cyber physical system , isolation (microbiology) , observer (physics) , key (lock) , smart grid , real time computing , detector , data mining , artificial intelligence , computer security , engineering , electrical engineering , microbiology and biotechnology , biology , telecommunications , physics , quantum mechanics , operating system
The emergence of cyber‐physical attacks brings a key challenge to the existing system integrity protection schemes (SIPSs) of smart grids. As one of typical cyber‐physical attacks, false data injection attacks (FDIAs) can bypass the existing Kalman filter‐based χ 2 ‐detector detection techniques in SIPSs. To improve the detection performance against the FDIAs in SIPSs, this study proposes an unknown input observer (UIO)‐based detection and isolation method. Taking the stealthy characteristics of FDIAs into account, this study presents a set of UIOs to detect the FDIA based on the internally physical dynamics. Furthermore, a UIO‐based detection and isolation algorithm against the FDIAs is proposed based on the feature of residuals generated by UIOs. To detect the cyber attacks quickly and avoid missing detection, an adaptive threshold is designed to replace the precomputed threshold by taking the model linearised error and disturbance into account. Finally, comprehensive simulation results on the proposed algorithm are carried out, and the effectiveness of improving the detection performance in SIPSs is verified.